Informatics
NASA used AI to design antennae in ways humans would not have considered.
Could the same approach be applied to drug design?
All we required was a dataset with hundreds of
thousands of molecules and their equivalent reduced graph outline to train the AI system. Fortunately, there are huge datasets of molecules readily available and generating high-level descrip- tions of a complete molecule is relatively easy. For any given reduced graph AI can propose new molecules which match the specification and which chemists can use to guide their search for the next drug candidate.
Becoming an AI-driven organisation For every success like the one above, there are many stories of AI failure. So how do we get it right? At a high level, AI success needs a mindset
change. It needs a willingness to take risks and step into new areas. Previous analytics were predictable and easy to understand. AI learns to recognise con- nections in data, but it is not always easy to see how it works. This creates fear of losing oversight and transparency. Researchers need to become comfortable working with this new approach. Deploying it in the correct way, as we will come to shortly, can go a long way to easing concerns. AI also needs innovative thinking and a willing-
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ness to learn from others in identifying how it can be deployed within an organisation, as we saw from the generative design algorithms used by NASA, and the use of language translation AI in drug design. Establishing a connection between apparently unrelated problems, such as drug dis- covery and language translation, may seem like a chance occurrence. But many successful applica- tions of AI come from examining related problems in other domains, and understanding how to extend them to new challenges. That said, humans will remain critical to build-
ing, training and overseeing AI. Drug chemistry is hard to predict and human experience will count for a long time. Good AI needs to learn its under- standing from human experts and the data they have created. Good models will still need chemists who can use their experience to assess potential problems before spending money to progress AI recommendations. Good AI deployment will require companies to build capability which mixes human expertise with understanding of data and technology. It will require training programmes for users to secure buy-in and ensure these new com- plex tools are used correctly.
Drug Discovery World Fall 2019
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